Multidimensional Analyses for High Confidence Untargeted Molecular Annotation using Structurally Specific Ion Mobility-Mass Spectrometry

Abstract

Mass spectrometry-based untargeted metabolomics aims to comprehensively characterize the varied and critical roles of molecular species in biological systems. Due to the complexity of the samples as well as the prevalence of isomers, confident annotation in global analyses remains a significant challenge. Ion mobility (IM) is a gas-phase separation technique that can be employed as a size-dependent separation strategy prior to mass spectrometric analysis to improve the separation of complex samples. In addition to providing a second dimension of separation, IM measurements can be used to calculate the collision cross-section (CCS) of the analyte, a highly reproducible molecular descriptor which can serve as an additional metric with which to identify specific analytes. To enhance the utility of CCS for untargeted identification workflows, a self-consistent database of over 3200 experimentally acquired CCS values was curated. This large database can be used to increase the confidence in compound identification, but there is additional applicability in the structural mass-mobility trends that emerge from these measurements. Using this global dataset and the predictive capabilities of statistical programming and regression strategies, empirical CCS information was used in a novel informatics workflow to increase annotation confidence based on chemical class. To increase the utility of IM workflows for more challenging isomeric separations, calibration methods for high resolution IM were explored and benchmarked. These efforts toward the reliable acquisition of high resolution CCS values support the characterization of previously unresolved isomeric species, providing increased specificity and structural resolution to facilitate high confidence in annotation and interpretation. Confident annotation of molecular species in untargeted studies will enable a more comprehensive understanding of dysregulation in large-scale biological processes.